WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality Technology provider, announced a blockchain-based trusted collaborative learning (TrusCL) framework for IoT which is a solution for the privacy-preserving and computational requirements of collaborative learning in AIoT environments. The TrusCL framework is combined with Homomorphic Encryption (HE) and Differential Privacy (DP) techniques, both of which are significant in the field of data protection and privacy enhancement.
Homomorphic encryption allows direct computation on encrypted data without the need to decrypt it first, which means that the data can be processed in an encrypted state, thus preventing data leakage at the source. This provides strong technical support for data privacy protection, allowing data owners to participate in the model training process while keeping data private. The differential privacy technique, on the other hand, further strengthens privacy by adding random noise to the dataset to ensure that even with an external query, no information about any particular individual can be accurately inferred. Combining homomorphic encryption and differential privacy, the TrusCL framework achieves a balance between privacy protection and model learning efficiency, which ensures the quality of model training and maintains the data privacy of the participants.
WiMi also added another layer of security by introducing blockchain technology into the TrusCL framework. All key activities of collaborative learning, including model updates, data contribution proofs and computation processes, will be recorded on the blockchain in a tamper-proof manner. This transparency and traceability effectively inhibit dishonest behavior, and any attempts to manipulate data or computation results will be promptly detected and blocked. The blockchain’s smart contracts also automatically enforce the terms of the agreement, ensuring that the participants’ computational behavior conforms to predefined rules, thus promoting a fair, transparent and trustworthy collaborative environment.
For model demanders, the blockchain-based IoT secure and trustworthy collaborative learning framework provides a secure and efficient platform that enables them to use sensitive data to securely train machine learning models without direct access to that data. The framework also supports dynamic adjustment of participants’ contribution and reward mechanisms to incentivize the contribution of more high-quality data and computational resources, further improving the accuracy and generalization of model training.
The WiMi‘s blockchain-based secure and trustworthy collaborative learning framework for IoT opens a path to higher security and trustworthiness for collaborative learning in the AIoT era through cutting-edge technology integration. With the in-depth application and continuous optimization of this framework, data sharing and intelligent analysis in AIoT will be more secure and efficient in the future, which will lay a solid foundation for the deepening development of Industry 4.0.
SOURCE: PRNewsWire